Lrnr_svm: Support Vector Machines
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Lrnr_svm

R Documentation

Support Vector Machines

Description

This learner provides fitting procedures for support vector machines, using the routines from e1071 (described in \insertCitee1071;textualsl3 and \insertCitelibsvm;textualsl3, the core library to which e1071 is an interface) through a call to the function svm.

Format

An R6Class object inheriting from Lrnr_base.

Value

A learner object inheriting from Lrnr_base with methods for training and prediction. For a full list of learner functionality, see the complete documentation of Lrnr_base.

Parameters

scale = TRUE: A logical vector indicating the variables to be scaled. For a detailed description, please consult the documentation for svm.
type = NULL: SVMs can be used as a classification machine, as a a regression machine, or for novelty detection. Depending of whether the outcome is a factor or not, the default setting for this argument is "C-classification" or "eps-regression", respectively. This may be overwritten by setting an explicit value. For a full set of options, please consult the documentation for svm.
kernel = "radial": The kernel used in training and predicting. You may consider changing some of the optional parameters, depending on the kernel type. Kernel options include: "linear", "polynomial", "radial" (the default), "sigmoid". For a detailed description, consult the documentation for svm.
fitted = TRUE: Logical indicating whether the fitted values should be computed and included in the model fit object or not.
probability = FALSE: Logical indicating whether the model should allow for probability predictions.
...: Other parameters passed to svm. See its documentation for details.

References

\insertAllCited

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_caret, Lrnr_cv_selector, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_density_discretize, Lrnr_density_hse, Lrnr_density_semiparametric, Lrnr_earth, Lrnr_expSmooth, Lrnr_gam, Lrnr_ga, Lrnr_gbm, Lrnr_glm_fast, Lrnr_glm_semiparametric, Lrnr_glmnet, Lrnr_glmtree, Lrnr_glm, Lrnr_grfcate, Lrnr_grf, Lrnr_gru_keras, Lrnr_gts, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_haldensify, Lrnr_hts, Lrnr_independent_binomial, Lrnr_lightgbm, Lrnr_lstm_keras, Lrnr_mean, Lrnr_multiple_ts, Lrnr_multivariate, Lrnr_nnet, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_polspline, Lrnr_pooled_hazards, Lrnr_randomForest, Lrnr_ranger, Lrnr_revere_task, Lrnr_rpart, Lrnr_rugarch, Lrnr_screener_augment, Lrnr_screener_coefs, Lrnr_screener_correlation, Lrnr_screener_importance, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_stratified, Lrnr_subset_covariates, Lrnr_tsDyn, Lrnr_ts_weights, Lrnr_xgboost, Pipeline, Stack, define_h2o_X(), undocumented_learner

Examples

data(mtcars)
# create task for prediction
mtcars_task <- sl3_Task$new(
  data = mtcars,
  covariates = c(
    "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
    "gear", "carb"
  ),
  outcome = "mpg"
)
# initialization, training, and prediction with the defaults
svm_lrnr <- Lrnr_svm$new()
svm_fit <- svm_lrnr$train(mtcars_task)
svm_preds <- svm_fit$predict()

jeremyrcoyle/sl3 documentation built on Nov. 18, 2024, 4:21 p.m.